Electrical & Computer Engineering - Systems

Courses

ECES 510 Analytical Methods in Systems 3.0 Credits

This course is intended to provide graduate student in the field of signal and image processing with the necessary mathematical foundation, which is prevalent in contemporary signal and image processing research and practice.

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

ECES 512 Fundamentals of Systems II 3.0 Credits

Core course. Covers realization and identification, including minimal realization, reducibility and equivalence of models, and identification of systems; stability, including bounded input-bounded output, polynomial roots, and Lyapunov; and feedback compensation and design, including observers and controllers and multi-input/multi-output systems.

This course introduces the field of detection and estimation and provides tools for classifying and learning about patterns in the face of total, partial or incomplete prior knowledge. Topics covered include Bayes classifier; Parametric estimation and supervised learning (MLE and Bayes Learning); Hypothesis testing; Decision Fusion; Unsupervised learning; and Non parametric testing.

Introduction to the computational modeling of sound and the human auditory system. Signal processing issues, such as sampling, aliasing, and quantization, are examined from an audio perspective. Covers applications including audio data compression (mp3), sound synthesis, and audio watermarking.

This course will continue the introduction to the emerging, multidisciplinary field of medical robotics. Topics include: medical procedure automation; robot testing and simulation techniques; This is a project based course that will afford students the opportunity to work with existing medical robotic systems.

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

ECES 614 Passive Network Synthesis 3.0 Credits

An introduction to approximation theory; driving point functions; realizability by lumped-parameter circuits; positive real functions; properties of two and three element driving point functions and their synthesis; transfer function synthesis; all-pass networks.

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

ECES 620 Multimedia Forensics and Security 3.0 Credits

This course introduces students to fundamental concepts in multimedia forensics and security. Topics covered include signal processing and machine learning techniques to detect forgeries, identify editing or manipulation, and determine the source of an image or video through direct signal analysis.

This course focuses on signal processing applied to analysis and design of biological systems. This is a growing area of interest with many topics ranging from DNA sequence analysis, to gene prediction, sequence alignment, and bio-inspired signal processing for robust system design.

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

ECES 641 Bioinformatics 3.0 Credits

This course will focus on developing the computational, algorithmic, and database navigational skills required to analyze genomic data that have become available with the development of high throughput genomic technologies. We will also illustrate statistical signal processing concepts such as dynamic programming, hidden markov models, information theoretic measures, and assessing statistical significance. The goals will be achieved through lecture and lab exercises that focus on genomic databases, genome annotation via hidden markov models, sequence alignment through dynamic programming, metagenomic analyses, and phylogenetics with maximum likelihood approaches.

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

ECES 642 Optimal Control 3.0 Credits

Introduces the concept of optimal control first by static optimization for state space formulated systems. The concept is expanded as the linear quadratic regulator problem for dynamic systems allowing solution of the optimal control and suboptimal control problems for both discrete and continuous time. Additional topics include the Riccati equation, the tracking problem, the minimum time problem, dynamic programming, differential games and reinforcement learning. The course focuses on deriving, understanding, and implementation of the algorithms.

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

ECES 650 Statistical Analysis of Genomics 3.0 Credits

This course focuses on the computational and statistical methods required to analyze metagenomic data. Students learn R and QIIME for conducting analyses. Students learn how to classify DNA sequences, distance and diversity metrics, ordination (ordering) techniques, and comparative statistical methods such as ANOVA and variations.

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

ECES 660 Machine Listening and Music IR 3.0 Credits

This course introduces methods for the computational analysis, recognition, and understanding of sound and music from the acoustic signal. Covered applications include sound detection and recognition, sound source separation, artist and song identification, music similarity determination, and automatic transcription.

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

ECES 681 Fundamentals of Computer Vision 3.0 Credits

Develops the theoretical and algorithmic tool that enables a machine (computer) to analyze, to make inferences about a "scene" from a scene's "manifestations", which are acquired through sensory data (image, or image sequence), and to perform tasks.

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

ECES 682 Fundamentals of Image Processing 3.0 Credits

The course introduces the foundation of image processing with hands-on settings. Taught in conjunction with an imaging laboratory.

This course is intended to produce students and image processing with a background on image formation in modalities for non-invasive 3D imaging. The goal is to develop models that lead to qualitative measures of image quality and the dependence of quality imaging system parameters.

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

ECES 685 Image Reconstruction Algorithms 3.0 Credits

This course is intended to provide graduate students in signal and image processing with an exposure to the design and evaluation of algorithms for tomographic imaging.

College/Department: College of EngineeringRepeat Status: Not repeatable for credit

ECES 817 Non-Linear Control Systems 3.0 Credits

Covers key topics of feedback linearization, sliding mode control, model reference adaptive control, self-tuning controllers and on-line parameter estimation. In addition additional no n-linear topics such as Barbalat’s Lemma, Kalman-Yakubovich Lemma, passivity, absolute stability, and establishing boundedness of signals are presented. The focus of the course is the understanding each of these algorithms in detail through derivation and their implementation through coding in Matlab and Simulink.

Writing-intensive Requirements

In order to graduate, all students must pass three writing-intensive courses after their freshman year. Two writing-intensive courses must be in a student's major. The third can be in any discipline. Students are advised to take one writing-intensive class each year, beginning with the sophomore year, and to avoid “clustering” these courses near the end of their matriculation. Transfer students need to meet with an academic advisor to review the number of writing-intensive courses required to graduate.

For additional information, and an up-to-date list of the writing-intensive courses being offered, students should check the Drexel University Writing Center page